Papers with dialog policy learning
Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots (2022.findings-emnlp)
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| Challenge: | Documents contain various structures that hinder the ability of machines to comprehend . user information needs are often underspecified, and the nature of heterogeneous documents poses challenges. |
| Approach: | They propose a dataset for building machines that help users seek information via conversations . their dataset contains over 100,000 turns based on Chinese documents from five domains . |
| Outcome: | The proposed tasks are challenging and worthy of further research. |
HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations (2021.acl-long)
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| Challenge: | Existing work on detecting user disengagement requires hand-labeling many dialog samples . Currently, self-reported user ratings are constrained to a static corpus . |
| Approach: | They propose an efficient annotation framework that denoises dialog samples instead of manually labeling them. |
| Outcome: | The proposed framework improves annotation efficiency significantly and detects user disengagement in two dialog corpora. |
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition (2021.emnlp-main)
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| Challenge: | Currently, most reinforcement learning methods for dialog policy learning train a centralized agent that selects a predefined joint action concatenating domain name, intent type, and slot name. |
| Approach: | They propose a hierarchical multi-agent framework in which each part of the action is led by a different agent and a joint optimization process that makes agents can exchange their policy information. |
| Outcome: | The proposed framework reduces labor costs for action templates and decreases the size of the action space for each agent. |